Incident category selection optimization

ABSTRACT

This disclosure describes techniques that enable a categorization controller to detect activation of a portable recording device that is configured to capture a real-time multimedia stream of the current event. The categorization controller may further identify a set of categories that are likely associated with the real-time multimedia stream, determine an ordered ranking of the set of categories, and generate a ranked category dataset for delivery to the portable recording device. In doing so, the portable recording device may present the ordered ranking of the set of categories at a user interface.

BACKGROUND

Law enforcement agencies are increasingly equipping their lawenforcement officers with portable recording devices. Such lawenforcement agencies may have policies manding that their lawenforcement officers use portable recording devices to recordinteractions with the public, to better serve and protect the publicfrom improper policing, as well as to protect law enforcement officersfrom false allegations of police misconduct.

In the context of law enforcement, incidents and events captured by aportable recording device need to be categorized to support timelyannotation and cross-referencing to incident report forms. However, thenumber of incident categories can become unwieldy, particularly whenusing a portable recording device which typically includes a limiteduser interface. Present-day, some portable recording devices include adial or a sliding tab user interface that permits users (e.g., lawenforcement officers) to scroll through a list of incident categoriesbefore making a selection. The selection process can at times provefrustrating, particularly since the list of incident categoriesavailable to a user, is long.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is set forth with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different figures indicates similaror identical items or features.

FIG. 1 illustrates an exemplary computing environment that facilitatesthe operation of the categorization controller.

FIG. 2 illustrates a block diagram of an operation of the categorizationcontroller in generating incident category data.

FIG. 3 illustrates a block diagram of various components of acategorization controller.

FIG. 4 illustrates an exemplary embodiment of a portable recordingdevice.

FIG. 5 illustrates a process for generating and transmitting incidentcategory data to a portable recording device based at least in part on aCAD identifier.

FIG. 6 illustrates a process for generating and transmitting incidentcategory data to a portable recording device based at least in part onenvironmental data.

FIG. 7 illustrates a process for generating and transmitting incidentcategory data to a portable recording device based at least in part onsensor data captured from a portable recording device.

FIG. 8 illustrates a process for generating and transmitting incidentcategory data to a portable recording device based at least in part onan audio data recording.

DETAILED DESCRIPTION

This disclosure describes techniques that enable a categorizationcontroller to detect activation of a portable recording device that isconfigured to capture a real-time multimedia stream of the currentevent. The categorization controller may further identify a set ofcategories that are likely associated with the real-time multimediastream, determine an ordered ranking of the set of categories, andgenerate a ranked category dataset for delivery to the portablerecording device. In doing so, the portable recording device may presentthe ordered ranking of the set of categories at a user interface.

The categorization controller may interact with the network operationscenter, the third-party server(s), and the portable recording device.The categorization controller may retrieve a computer-aided dispatch(CAD) identifier from the network operations center that is associatedwith a real-time incident. In one embodiment, the categorizationcontroller may monitor and poll the network operations center to captureCAD identifiers. In another embodiment, the network operations centermay push a newly generated CAD identifier to the categorizationcontroller. The network operations center may assign a CAD identifier toeach real-time incident. In one example, the CAD identifier may identifythe incident type. Without limitation, the incident type may be adomestic disturbance, a traffic infraction, violent dispute, propertydamage, trespass, protest, or public nuisance. In this example, multiplereal-time incidents may retain the same CAD identifier, based on thesimilarity of the incident type.

In another example, the CAD identifier may comprise a character stringof two portions. The first portion may be associated with the incidenttype, and the second portion may uniquely distinguish the real-timeincident from prior, successive, and contemporaneous real-timeincidents. In this way, the CAD identifier may describe the incidenttype, and simultaneously distinguish a particular real-time incidentfrom prior, successive, and contemporaneous real-time incidents.

The categorization controller may retrieve environmental data from athird-party server(s), third-party news reports, social media postings,weather reports, or the like, which describe the disposition of asurrounding environment and the real-time events that are proximate tothe geolocation of the real-time incident. The categorization controllermay first infer the geolocation of the portable recording device 106that has been dispatched to a real-time incident, and then use thegeolocation to capture environmental data from a third-party server(s).

The categorization controller may monitor and retrieve sensor data fromthe portable recording device. The sensor data may include thegeolocation of the portable recording device (which, by extension,corresponds to a geolocation of the real-time incident), audio datarecorded via the portable recording device by the law enforcementofficer, and a real-time multimedia stream of the surroundingenvironment proximate to the portable recording device (which, byextension, corresponds to a real-time multimedia stream of the real-timeincident).

The monitoring activity may occur continuously, per a predeterminedschedule, or in response to a triggering event. Continuous monitoringoccurs after the user (e.g., law enforcement officer) activates theportable recording device upon arrival at the geolocation of thereal-time incident. Monitoring per a predetermined schedule maycorrespond to a monitoring activity occurring at any time interval, suchas one minute, five minutes, 20 minutes, 30 minutes, or one hour.Monitoring in response to a triggering event may correspond tomonitoring occurring in response to receipt of a message from thenetwork operations center indicating that the portable recording device(and law enforcement officer) has been dispatched to a real-timeincident at the geolocation.

The categorization controller may analyze at least one of the CADidentifier, the environmental data, or the sensor data to generateincident category data. The incident category data may comprise anordered ranking of incident categories that likely describe thereal-time incident. The incident category data may further includecomputer-executable instructions that present an ordered ranking ofincident categories on a user interface (e.g., category wheel) of theportable recording device.

Further, the term “techniques,” as used herein, may refer to system(s),method(s), computer-readable instruction(s), module(s), algorithms,hardware logic, and/or operation(s) as permitted by the contextdescribed above and through the document.

FIG. 1 illustrates an exemplary computing environment that facilitatesthe operation of the categorization controller. The computingenvironment 100 may comprise a network operations center 102, acategorization controller 104, a portable recording device 106, andthird-party server(s) 108(1)-108)N), each of which is operably connectedvia one or more network(s) 110.

The network operations center 102 may be configured to control thedispatch of law enforcement officers to real-time incidents. Further,the network operations center 102 may assign a computer-aided dispatch(CAD) identifier to each real-time incident. Each CAD identifier may beconfigured to uniquely identify an incident type. Without limitation,the incident type may be a domestic disturbance, a traffic infraction,violent dispute, property damage, trespass, protest, or public nuisance.In another embodiment, the CAD identifier may comprise a characterstring that comprises two portions. The first portion may be associatedwith the incident type, and the second portion may uniquely distinguishthe real-time incident from prior and successive real-time incidents. Inthis way, the CAD identifier may describe the incident type, andsimultaneously distinguish a particular real-time incident from prior,successive, and contemporaneous real-time incidents.

The third-party server(s) 108(1)-108(N) may comprise remote servers thatprovide environmental data associated with a real-time incident. In someembodiments, the categorization controller 104 may interact with thethird-party server(s) 108(1)-108(N) to capture environmental data basedon the geolocation of a portable recording device 106 that has beendispatched to a real-time incident. In one example, the geolocation ofthe portable recording device 106 is implied to correlate with thegeolocation of the real-time incident. Environmental data may includethird-party news reports, social media posting, weather reports, or thelike, which describe the disposition of a surrounding environment andthe real-time events that are occurring proximate to the currentgeolocation of the portable recording device.

The portable recording device 106 may be worn by a user 112, such as alaw enforcement officer. The portable recording device 106 may be aportable video recording device, a portable audio recording device, or aportable multimedia recording device that captures sensor data from asurrounding environment proximate to the portable recording device 106.In one embodiment, the portable recording device may capture image data(e.g., in-motion video image data or still-motion image data) and audiodata via one or more sensors (e.g., video image capturing component andmicrophone, respectively). Further, the portable recording device 106may capture the current geolocation via a GPS sensor. Accordingly, theone or more sensors may include, without limitation, a microphone, avideo image capturing component, and a GPS sensor. The portablerecording device 106 may include onboard memory that stores instances ofcaptured GPS data, audio data, image data, or multimedia data stream.The portable recording device 106 may be manually activated to capture amultimedia stream by the user 112 (e.g., law enforcement officer). Forexample, the portable recording device 106 may include an inputinterface (e.g., physical buttons, a gesture recognition mechanism, avoice activation mechanism) that enables the user 112 (e.g., lawenforcement officer) to start, stop, and/or pause the recording of thereal-time data.

The categorization controller 104 may be configured to determine anordered ranking of incident categories associated with a real-timeincident. In some examples, the categorization controller 104 maydetermine the ordered ranking based on a CAD identifier from the networkoperations center 102, environmental data from the third-party server(s)108(1)-108(N), sensor data from a portable recording device 106, or anysuitable combination thereof.

The categorization controller 104 may operate on one or more distributedcomputing resource(s). The one or more distributed computing resource(s)may include one or more computing device(s) that operate in a cluster orother configuration to share resources, balance load, increaseperformance, provide fail-over support or redundancy, or for otherpurposes. The one or more computing device(s) may include one or moreinterfaces to enable communications with other networked devices, suchas the portable recording device 106 via the one or more network(s) 110.

The one or more network(s) 110 may include public networks such as theInternet, private networks such as an institutional and/or personalintranet, or some combination of a private and public network(s). Theone or more network(s) can also include any suitable type of wiredand/or wireless network, including but not limited to local area network(LANs), wide area network(s) (WANs), satellite networks, cable networks,Wi-Fi networks, Wi-Max networks, mobile communications networks (e.g.,5G-NR, LTE, 3G, 2G), or any suitable combination thereof.

FIG. 2 illustrates a block diagram of an operation of the categorizationcontroller in generating incident category data. In the illustratedexample, the categorization controller 104 may interact with the networkoperations center 102, the third-party server(s) 108(1)-108)N), and theportable recording device 106. The categorization controller 104 mayretrieve a computer-aided dispatch (CAD) identifier 202 from the networkoperations center 102 that is associated with a real-time incident. Inone embodiment, the categorization controller 104 may monitor and pollthe network operations center to capture CAD identifiers. In anotherembodiment, the network operations center 102 may push a newly generatedCAD identifier 202 to the categorization controller 104. The networkoperations center 102 may assign a CAD identifier 202 to each real-timeincident. In one example, the CAD identifier 202 may identify theincident type. Without limitation, the incident type may be a domesticdisturbance, a traffic infraction, violent dispute, property damage,trespass, protest, or public nuisance. In this example, multiplereal-time incidents may retain the same CAD identifier 202, based on thesimilarity of the incident type.

In another example, the CAD identifier 202 may comprise a characterstring of two portions. The first portion may be associated with theincident type, and the second portion may uniquely distinguish thereal-time incident from prior, successive, and contemporaneous real-timeincidents. In this way, the CAD identifier may describe the incidenttype, and simultaneously distinguish a particular real-time incidentfrom prior, successive, and contemporaneous real-time incidents.

The categorization controller 104 may retrieve environmental data 204from the third-party server(s) 108(1)-108(N). Environmental data 204 mayinclude third-party news reports, social media postings, weatherreports, or the like, which describe the disposition of a surroundingenvironment and the real-time events that are proximate to thegeolocation of the real-time incident. The categorization controller 104may first infer the geolocation of the portable recording device 106that has been dispatched to a real-time incident, and then use thegeolocation to capture environmental data from the third-partyserver(s).

The categorization controller 104 may monitor and retrieve sensor data206 from the portable recording device. The sensor data 206 may includethe geolocation of the portable recording device (which, by extension,corresponds to a geolocation of the real-time incident), audio datarecorded via the portable recording device by the law enforcementofficer, and a real-time multimedia stream of the surroundingenvironment proximate to the portable recording device (which, byextension, corresponds to a real-time multimedia stream of the real-timeincident).

The monitoring activity may occur continuously, per a predeterminedschedule, or in response to a triggering event. Continuous monitoringoccurs after the user (e.g., law enforcement officer) activates theportable recording device upon arrival at the geolocation of thereal-time incident. Monitoring per a predetermined schedule maycorrespond to a monitoring activity occurring at any time interval, suchas one minute, five minutes, 20 minutes, 30 minutes, or one hour.Monitoring in response to a triggering event may correspond tomonitoring occurring in response to receipt of a message from thenetwork operations center 102 indicating that the portable recordingdevice 106 (and law enforcement officer) has been dispatched to areal-time incident at the geolocation.

At block 210, the categorization controller 104 may analyze at least oneof the CAD identifier 202, the environmental data 204, or the sensordata 206 to generate incident category data 208. The incident categorydata 208 may comprise an ordered ranking of incident categories thatlikely describe the real-time incident. The incident category data 208may further include computer-executable instructions that present anordered ranking of incident categories on a user interface (e.g.,category wheel) of the portable recording device.

FIG. 3 illustrates a block diagram of various components of acategorization controller. The categorization controller 104 isconfigured to generate an ordered ranking of incident categories thatare associated with a real-time incident. The categorization controller104 may include input/output interface(s) 302. The input/outputinterface(s) 302 may include any suitable type of output interface knownin the art, such as a display (e.g., a liquid crystal display),speakers, a vibrating mechanism, or a tactile feedback mechanism.Input/output interface(s) 302 also includes ports for one or moreperipheral devices, such as headphones, peripheral speakers, or aperipheral display. Further, the input/output interface(s) 302 mayfurther include a camera, a microphone, a keyboard/keypad, or atouch-sensitive display. A keyboard/keypad may be a push-buttonnumerical dialing pad (such as on a typical telecommunication device), amulti-key keyboard (such as a conventional QWERTY keyboard), or one ormore other types of keys or buttons, and may also include ajoystick-like controller and/or designated navigation buttons, or thelike.

Additionally, the categorization controller 104 may include networkinterface(s) 304. The network interface(s) 304 may include any suitablesort of transceiver known in the art. For example, the networkinterface(s) 304 may include a radio transceiver that performs thefunction of transmitting and receiving radio frequency communicationsvia an antenna. Also, the network interface(s) 304 may include awireless communication transceiver and a near-field antenna forcommunicating over unlicensed wireless Internet Protocol (IP) networks,such as local wireless data networks and personal area networks (e.g.,Bluetooth or near field communication (NFC) networks). Further, thenetwork interface(s) 304 may include wired communication components,such as an Ethernet port or a Universal Serial Bus (USB). Hardwarecomponent(s) 306 may include additional hardware interface, datacommunication hardware, and data storage hardware.

Further, the categorization controller 104 may include one or moreprocessor(s) 308 that are operably connected to memory 310. In at leastone example, the one or more processor(s) 308 may be a centralprocessing unit(s) (CPU), graphics processing unit(s) (GPU), or both aCPU and GPU or any suitable sort of processing unit(s). Each of the oneor more processor(s) 308 may have numerous arithmetic logic units (ALUs)that perform arithmetic and logical operations as well as one or morecontrol units (CUs) that extract instructions and stored content fromprocessor cache memory, and then execute these instructions by callingon the ALUs, as necessary during program execution. The one or moreprocessor(s) 308 may also be responsible for executing all computerapplications stored in the memory, which can be associated with commontypes of volatile (RAM) and/or non-volatile (ROM) memory.

In some examples, memory 310 may include system memory, which may bevolatile (such as RAM), non-volatile (such as ROM, flash memory, etc.),or some combination of the two. The memory may also include additionaldata storage devices (removable and/or non-removable) such as, forexample, magnetic disks, optical disks, or tape.

The memory 310 may further include non-transitory computer-readablemedia, such as volatile and nonvolatile, removable, and non-removablemedia implemented in any suitable method or technology for storage ofinformation, such as computer-readable instructions, data structures,program modules, or other data. System memory, removable storage, andnon-removable storage are all examples of non-transitorycomputer-readable media. Examples of non-transitory computer-readablemedia include, but are not limited to, RAM, ROM, EEPROM, flash memory orother memory technology, CD-ROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage, or other magnetic storage devices, or any suitablenon-transitory medium which can be used to store the desiredinformation.

In the illustrated example, the memory 310 may include an operatingsystem 312, an interface module 314, a category identification module316, an ordered ranking module 318, a notification module 320, and adata store 322. The operating system 312 may be any suitable operatingsystem capable of managing computer hardware and software resources. Theoperating system 312 may include an interface layer that enablesapplications to interface with the input/output interface(s) 302 and thenetwork interface(s) 304.

The interface module 314 may be configured to interact with the networkoperations center to capture a CAD identifier associated with areal-time incident, and further interact with the third-party server(s)to capture environmental data associated with a real-time incident.Further, the interface module 314 may interact with the portablerecording device(s) to capture sensor data associated with a real-timeincident. Particularly, the interface module 314 may monitor theportable recording device to capture the sensor data at the geolocation,or proximate to the geolocation, of a real-time incident. The monitoringactivity may occur continuously, per a predetermined schedule, or inresponse to a triggering event. Continuous monitoring implies that theuser (e.g., law enforcement officer) activates the portable recordingdevice upon arrival at the geolocation of the real-time incident. Thepredetermined schedule may correspond to any time interval, such as oneminute, five minutes, 20 minutes, 30 minutes, or one hour. Thetriggering event may correspond to receipt of a message from the networkoperations center indicating that the portable recording device (and lawenforcement officer) has been dispatched to a real-time incident at thegeolocation.

The interface module 314 may further transmit incident category data toa portable recording device. The incident category data may includecomputer-executable instructions that present an ordered ranking ofincident categories on a user interface (e.g., category wheel) of theportable recording device.

The category identification module 316 may be configured to generate alist of incident categories that are likely associated with a real-timeincident. The category identification module 316 may further include aCAD ID component 324, an environmental data component 326, and a sensordata component 328. The CAD ID component 324 may be configured toanalyze a CAD identifier to determine a listing of incident categories.For example, the network operations center may issue a CAD identifierfor each real-time incident. Each CAD identifier may be configured touniquely identify an incident type associated with a real-time incident.Without limitation, the incident type may be a domestic disturbance, atraffic infraction, violent dispute, property damage, trespass, protest,or public nuisance. Accordingly, the CAD ID component 324 may analyzethe CAD identifier to infer one or more incident categories.Particularly, the CAD ID component 324 may compare the CAD identifier todata entries within a predefined CAD-incident type data set to infer oneor more incident categories. The predefined CAD-incident type data setmay comprise a data structure that correlates individual CADidentifiers, or portion thereof (e.g., if the CAD identifier comprisestwo portions, the data structure would include a correlation to thefirst portion that is associated with the incident type) withcorresponding incident types.

The environmental data component 326 may be configured to interact withthe third-party server(s) to capture environmental data associated witha real-time incident. The environmental data component 326 may determinethe geolocation of a portable recording device, and in doing so,interrogate third-party server(s) to capture environmental dataassociated with the geolocation. The environmental data may correspondto third-party news reports, social media postings, weather reports, orthe like, which describe the disposition of a surrounding environmentand the real-time events that are proximate to the geolocation of thereal-time incident. In one embodiment, the environmental data component326 may infer the geolocation of the real-time incident based on thegeolocation of the portable recording device (e.g., captured via sensordata from the portable recording device) that has been dispatched to areal-time incident. In another embodiment, the environmental datacomponent 326 may infer the geolocation of the real-time incident basedon receipt of a message from the network operations center indicatingthat the portable recording device (and law enforcement officer) hasbeen dispatched to a real-time incident at the geolocation.

The sensor data component 328 may be configured to analyze sensor dataretrieved from the portable recording device to infer one or moreincident categories. The sensor data may include the geolocation of theportable recording device (which, by extension, corresponds to ageolocation of the real-time incident), audio data recorded via theportable recording device by the user (e.g., law enforcement officer),and a real-time multimedia stream of the surrounding environmentproximate to the portable recording device (which, by extension,corresponds to a real-time multimedia stream of the real-time incident).

In one embodiment, the category identification module 316 may employ oneor more trained machine-learning algorithms to infer a list of incidentcategories that are likely associated with the real-time incident. Thecategory identification module 316 may generate the data model usinghistorical incident data associated with historical instances ofreal-time incidents. Each historical instance of real-time incidents mayinclude at least one of the CAD identifier, environmental data, sensordata, or any suitable combination thereof.

Accordingly, the category identification module 316 may correlate inputdata associated with a real-time incident (e.g., CAD identifier, sensordata, environmental data, or any suitable combination thereof) with datapoints of the data model to infer a likely list of incident categoriesassociated with the real-time incident.

The ordered ranking module 318 may be configured to analyze the listingof one or more incident categories to infer an ordered ranking ofincident categories. A superior ranking to afforded to the incidentcategory that is most likely associated with the real-time incident. Insome embodiments, the ordered ranking module 318 may employ one or moretrained machine-learning algorithms to generate a data model to inferthe ordered ranking of incident categories. The ordered ranking module318 may generate the data model using historical incident dataassociated with historical real-time incidents. The historical incidentdata may include a correlation of incident categories with historicalinstances of real-time incidents. Each historical instance of real-timeincidents may include at least one of the CAD identifier, environmentaldata, sensor data, or any suitable combination thereof.

Accordingly, the ordered ranking module 318 may correlate input dataassociated with a real-time incident (e.g., list of incident categories,CAD identifier, sensor data, environmental data, or any suitablecombination thereof) with data points of the data model to infer alikely ordered ranking of incident categories associated with thereal-time incident.

Further, the ordered ranking module 318 may assign accuracy scores toindividual incident categories, based at least in part on the data modelanalysis. The accuracy score may reflect a quantitative measure of thelikelihood that the incident category corresponds to a real-timeincident. The accuracy score may be alpha-numeric (e.g., 0 to 10, or Ato F), descriptive (e.g., low, medium, or high), based on color, (i.e.red, yellow, or green), or any other suitable rating scale. A high(e.g., superior) accuracy score (e.g., 7 to 10, high, or green) mayreflect an inference that the incident category is likely associatedwith the real-time incident. A medium accuracy score (e.g., 4 to 6,medium, or yellow) may reflect an inference that the incident categorymay or may not be associated with the real-time incident. A low accuracyscore (e.g., 1 to 3, low, or red) may reflect an inference that theincident category is unlikely associated with the real-time incident.

The notification module 320 may further include a communication managercomponent 330 that is configured to generate the incident category datafor delivery to a portable recording device. The notification module 320may interact with the ordered ranking module 318 to retrieve the orderedranking of incident categories. The incident category data may includecomputer-executable instructions that are configured to present theordered ranking of the incident categories via a user interface (e.g.,category selector) of the portable recording device. For example,incident category data may be configured such that the first selectionvia a user interface of the portable recording device corresponds to thehighest-ranking incident category of the ordered ranking of incidentcategories.

The data store 322 may include a repository of CAD identifiers from thenetwork operations center, a predefined CAD-incident type data set,sensor data from portable recording device(s), environmental data fromthe third-party server(s), historical instances of incident categorydata, and any other data pertinent to an operation of the categorizationcontroller 104.

The categorization controller 104, via various modules and components,may make use of one or more trained machine-learning algorithms such assupervised learning, unsupervised learning, semi-supervised learning,naive Bayes, Bayesian network, decision trees, neural networks, fuzzylogic models, and/or probabilistic classification models.

FIG. 4 illustrates an exemplary embodiment of a portable recordingdevice. In the illustrated example, the portable recording device 106may include a video image capturing component 402 (e.g., camera) forcapturing video image data, a microphone 404 for capturing audio data,and a speaker 406 for broadcasting audio data. The portable recordingdevice may include a category selector. In the illustrated embodiment,the category selector may correspond to a category wheel 408. In otherembodiments, the category selector may comprise any other form, such asa category slide. The category wheel 408 may include an indicator tab410 that can align with preset radial position(s) 412 on the front faceof the portable recording device 106. The indicator tab 410 may beconfigured to stop at a predetermined number of preset radialposition(s) 412. Each of the preset radial position(s) 412 maycorrespond to a category classification that a user (e.g., lawenforcement officer) may assign to a multimedia stream (e.g., real-timeincident) captured by the portable recording device 106.

In one embodiment, the user (e.g., law enforcement officer) may use thecategory wheel 408 to assign an incident category before capturing amultimedia stream. In another embodiment, the user may use the categorywheel 408 to assign an incident category while the multimedia stream isbeing captured. In yet another embodiment, an incident category may beassigned within a predetermined time period after the multimedia streamwas captured. In each of these embodiments, once the multimedia streamhas been captured, a controller (e.g., category association module 414)within the portable recording device 106 may execute softwareinstructions that associate the incident category to the multimediastream.

The portable recording device 106 may further include an activationbutton 416. The activation button 416 may be configured to cause theportable recording device 106 to perform one or more actions that dependon a present configuration of the portable recording device 106. The oneor more actions may include starting and stopping the capturing of amultimedia stream, marking a point of interest while capturing amultimedia stream, or confirming a selection may be a user (e.g., lawenforcement officer) while using the portable recording device 106.

The portable recording device may include one or more sensor(s) 418.Without limitation, the one or more sensor(s) 418 may include themicrophone 404, the video image capturing component 402, and a GPSsensor.

The portable recording device 106 may interact with the networkoperations center 102 and the categorization controller 104 via networkinterface(s) 420. The network interface(s) 420 may functionallycorrespond to the network interface(s) 304. The portable recordingdevice 106 may further include input/output interface(s) 422. Theinput/output interface(s) 422 may be functionally similar to theinput/output interface(s) 302.

Additionally, the portable recording device 106 may include hardwarecomponent(s) 424, which are functionally similar to the hardwarecomponent(s) 306, and one or more processor(s) 426 operably connected tomemory 428. The one or more processor(s) 426 may functionally correspondto the one or more processor(s) 308, and the memory 428 may functionallycorrespond to the memory 310. The memory 428 may further include anoperating system 430, interface module 432, sensor data module 434,category selection module 436, a category association module 414, and adata store 438. The operating system 430 may be any operating systemcapable of managing computer hardware and software resources. Theinterface module 432 may be configured to interface with the networkoperations center 102 to receive a CAD identifier. Further, theinterface module 432 may interface with the categorization controller104 to receive incident category data and further transmit sensor datato the categorization controller 104. Without limitation, the sensordata may include geolocation data, audio data, video data, or anysuitable combination thereof.

The sensor data module 434 may be configured to aggregate sensor datacaptured from the one or more sensor(s) 418. The category selectionmodule 436 may be configured to interpret incident category datareceived from the categorization controller 104 to present an orderedranking of incident categories on the category wheel. The categoryselection module 436 may assign the highest-ranking incident category ofthe ordered ranking of incident categories to a first selection on thecategory wheel 408. Subsequent selections on the category wheel 408 maybe assigned subsequently ranked incident categories. A user maymanipulate the category wheel 408 to move the indicator tab 410 to apreset position that corresponds to a chosen incident category. The user(e.g., law enforcement officer) may confirm a selection of a chosenincident category by pressing the activation button 416. In someembodiments, the category selection module 436 may cause the speaker 406to annunciate an incident category associated with a preset position, ata point in time when the indicator tab 410 of the category wheel 408aligns with the preset position.

The category association module 414 may be configured to executesoftware instructions that associate a selected incident category to themultimedia stream (e.g., sensor data) associated with the real-timeincident.

Further, the data store 438 may include a repository of sensor data,incident category data, and any other suitable data pertinent to anoperation of the portable recording device 106.

FIGS. 5 through 8 present processes 500, 600, 700, and 800 that relateto operations of the categorization controller 104. Each of theprocesses 500, 600, 700, and 800 illustrate a collection of blocks in alogical flow chart, which represents a sequence of operations that canbe implemented in hardware, software, or a combination thereof. In thecontext of software, the blocks represent computer-executableinstructions that, when executed by one or more processors, perform therecited operations. Generally, computer-executable instructions mayinclude routines, programs, objects, components, data structures, andthe like that perform particular functions or implement particularabstract data types. The order in which the operations are described isnot intended to be construed as a limitation, and any number of thedescribed blocks can be combined in any order and/or in parallel toimplement the process. For discussion purposes, the processes 500, 600,700, and 800 are described with reference to the computing environment100 of FIG. 1 .

FIG. 5 illustrates a process for generating and transmitting incidentcategory data to a portable recording device based at least in part on aCAD identifier. Process 500 is described from the perspective of thecategorization controller. In process 500, the categorization controllermay interact with a network operations center, the portable recordingdevice, and third-party server(s) that capture environmental data.

At 502, the categorization controller may receive from a networkoperations center, a computer-aided dispatch (CAD) identifier associatedwith a real-time incident. Here, the network operations center mayassign a CAD identifier to each real-time incident. In one example, thenetwork operations center may assign each real-time incident a CADidentifier that corresponds to the incident type. Without limitation,the incident type may be a domestic disturbance, a traffic infraction,violent dispute, property damage, trespass, protest, or public nuisance.In this example, multiple real-time incidents may retain the same CADidentifier, based on the similarity of the incident type.

In another example, the CAD identifier may comprise a character stringof two portions. The first portion may be associated with the incidenttype, and the second portion may uniquely distinguish the real-timeincident from prior, successive, and contemporaneous real-timeincidents. In this way, the CAD identifier may describe the incidenttype, and simultaneously distinguish a particular real-time incidentfrom prior, successive, and contemporaneous real-time incidents.

At 504, the categorization controller may identify a listing of one ormore one or more incident categories that likely describe the real-timeincident, based at least in part on the CAD identifier. In this example,the categorization controller may analyze the CAD identifier to identifythe incident type, and in doing so, assign one or more incidentcategories. The incident category may be a granular representation ofthe incident type. For example, consider a real-time incident associatedwith a traffic infraction. While the incident type broadly envelopes aplethora of possible events, the incident categories aim to provide agranular selection of possible events. For example, of a trafficinfraction incident type, the incident categories may include but arenot limited to a speeding vehicle, a Driving Under the Influence (DUI)arrest, a combative DUI, a single-vehicle accident, multiple vehicleaccident, a vehicle accident with injured persons, and so forth.

In this process step, the categorization controller may correlate theCAD identifier with a set of predefined CAD-incident type data set toidentify the incident category. The predefined CAD-incident type dataset may comprise a data structure that correlates individual CADidentifiers, or portion thereof (e.g., if the CAD identifier comprisestwo portions, the data structure would include a correlation to thefirst portion that is associated with the incident type) withcorresponding incident types.

At 506, the categorization controller may employ one or moremachine-learning algorithms to determine an ordered ranking of incidentcategories. The ordered ranking is intended to represent the likelihoodthat a real-time incident corresponds to a particular incident category.In one embodiment, the likelihood of occurrence may be based on theprevalence of one incident category occurring over others within anincident type, over a predetermined time period. Referring to thetraffic infraction incident type, the most common incident category maybe a “speeding vehicle” and the least common may be “multiple vehicleaccident with injured persons.” In this example, the categorizationcontroller may rank the “speeding vehicle” incident category as mostlikely and the “multiple vehicle accident with injured persons” incidentcategory as the least likely.

In another embodiment, the categorization controller may determine theordered ranking of incident categories based on additional data. Forexample, the categorization controller may capture sensor data from aportable recording device (e.g., worn by a law enforcement officer) thathas been dispatched to the real-time incident. When the law enforcementofficer is at the scene of the real-time incident, the sensor data mayinclude the geolocation of the portable recording device (which, byextension, corresponds to a geolocation of the real-time incident),audio data recorded via the portable recording device by the lawenforcement officer, and a real-time multimedia stream of thesurrounding environment proximate to the portable recording device(which, by extension, corresponds to a real-time multimedia stream ofthe real-time incident).

In this embodiment, the categorization controller may employ one or moremachine-learning algorithms to analyze the sensor data to infer a likelyordered ranking of incident categories. The likely ordered ranking maybe based on analysis of a multimedia stream captured from the portablerecording device. For example, the categorization controller may employnatural language processing (NLP) and natural language understanding(NLU) algorithms to infer the meaning of captured speech. In doing so,the categorization controller may infer the likely nature of theincident, which in turn, may influence the ordered ranking of theincident categories. Here, the categorization controller may infer thatthe incident is likely a DUI based on analysis of speech, but also inferthat the DUI is likely combative based on the tone of speech or natureof word selection.

Similarly, the categorization controller may employ one or moremachine-learning algorithms to infer the ordered ranking of incidentcategories based on the historical prevalence of particular incidentcategories occurring at the geolocation. For example, the categorizationcontroller may glean the geolocation of the real-time incident from thenetwork operations center, the CAD identifier, or the sensor data fromthe portable recording device. In doing so, the categorizationcontroller may analyze historical incidents to infer a likely incidentcategory for the real-time incident. For example, the categorizationcontroller may determine that the geolocation corresponds to a stretchof highway where vehicles often exceed the posted speed limit. In doingso, the categorization controller may infer that the likely incidentcategory at the given geolocation is a speeding vehicle.

In another embodiment, the categorization controller may retrieveenvironmental data from the third-party server(s) to infer an orderedranking of incident categories. The environmental data may correspond tothird-party news reports, social media postings, weather reports, or thelike, which describe the disposition of a surrounding environment andthe real-time events that are occurring proximate to the geolocation ofthe real-time incident. For example, the categorization controller maycapture environmental data from social media postings that describe aviolent protest at a commercial establishment that is proximate to thegeolocation of the real-time incident. In this example, thecategorization controller may infer that the likely incident category is“trespass.” Without limitation, other likely incident categories mayinclude “public nuisance,” and “property damage.” The categorizationcontroller may infer a likely ordered ranking of the incident categories(e.g., trespass, public nuisance, and property damage) based on an NLPand NLU analysis of the social media postings.

At 508, the categorization controller may generate incident categorydata that comprises the ordered ranking of the incident categories. Theincident category data may include one incident category, or an orderedranking of two incident categories, five incident categories, or tenincident categories. An ordered ranking of any number of incidentcategories is possible.

At 510, the categorization controller may transmit the incident categorydata to the portable recording device that has been dispatched to thereal-time incident. The incident category data may includecomputer-executable instructions that are configured to present theordered ranking of the incident categories via a user interface of theportable recording device. In one embodiment, the user interface maycomprise a category wheel, as illustrated in FIG. 4 . In anotherembodiment the user interface may comprise a category slide. Any userinterface is possible, provided the user interface can facilitate theselection of an incident category from a plurality of incidentcategories.

The incident category data may be configured such that the firstselection via the user interface of the portable recording devicecorresponds to the highest-ranked incident category of the orderedranking of incident categories. For example, if the categorizationcontroller infers that a real-time incident is likely a “speedingvehicle” then the first selection presented via the user interface ofthe portable recording device may correspond to a “speeding vehicle”incident category.

FIG. 6 illustrates a process for generating and transmitting incidentcategory data to a portable recording device based at least in part onenvironmental data. Process 600 is described from the perspective of thecategorization controller. In process 600, the categorization controllermay interface with a network operations center, a portable recordingdevice, and third-party server(s) that capture the environmental data.

At 602, the categorization controller may monitor the geolocation of aportable recording device associated with a user (e.g., law enforcementofficer) dispatched to a real-time incident. Monitoring may occurcontinuously, per a predetermined schedule, or in response to atriggering event. Without limitation, the predetermined schedule maycorrespond to a time interval of one minute, five minutes, 20 minutes,30 minutes, or one hour. Any time interval is possible. The triggeringevent may correspond to receipt of a message from the network operationscenter indicating that the portable recording device (and lawenforcement officer) has been dispatched to a real-time incident at thegeolocation.

At 604, the categorization controller may receive from a third-partyserver(s), environmental data associated with the geolocation of aportable recording device. In this example, the environmental data maycorrespond to third-party news reports, social media posting, weatherreports, or the like, that describe the disposition of a surroundingenvironment and the real-time events that are occurring proximate to thecurrent geolocation of the portable recording device.

In other embodiments, the categorization controller may receiveadditional data associated with the geolocation of the portablerecording device. Additional data may include a CAD identifier from thenetwork operations center, sensor data from the portable recordingdevice, or a suitable combination of both.

At 606, the categorization controller may employ one or moremachine-learning algorithms to infer a listing of one or more incidentcategories that likely describe the real-time incident, based at leastin part on the environmental data. For example, if news reports describea “multi-vehicle collision” at the geolocation that corresponds to theportable recording device, the categorization controller may infer,without limitation, that the one or more incidents may include“multi-vehicle collision,” “multi-vehicle collision with propertydamage,” “multi-vehicle collision with injured persons,” “multi-vehiclecollision with injured persons and property damage,” and any othersuitable incident category.

In other embodiments, the categorization controller may infer thelisting of one or more incident categories, based on the environmentaldata and additional data (e.g., CAD identifier, sensor data, or asuitable combination of both).

At 608, the categorization controller may further employ one or moremachine-learning algorithms to infer an ordered ranking of the incidentcategories, based at least in part on the environment data. The orderedranking is intended to represent the likelihood that the real-timeincident corresponds to a particular incident category. For example, ifa news report describes a multi-vehicle collision with injured persons,then the categorization controller may infer that the likely incidentcategory corresponds to “multi-vehicle collision with injured persons.”In this example, the categorization controller may assign a superiorranking to the “multi-vehicle collision with injured persons” incidentcategory. The remaining incident categories may be assigned inferiorordered rankings based on analysis of the environmental data. Forexample, if the multi-vehicle collision occurred in a densely populatedgeolocation, the next likely incident category may be “multi-vehiclecollision with injured persons and property damage,” and so forth.

In other embodiments, the categorization controller may infer theordered ranking of incident categories, based on the environmental dataand additional data (e.g., CAD identifier, sensor data, or a suitablecombination of both).

At 610, the categorization controller may generate incident categorydata that comprises the ordered ranking of the incident categories. Theincident category data may include one incident category or an orderedranking of any number of incident categories.

At 612, the categorization controller may transmit the incident categorydata to the portable recording device that has been dispatched to thereal-time incident. The incident category data may includecomputer-executable instructions that are configured to present theordered ranking of the incident categories via a user interface (e.g.,category selector) of the portable recording device. The incidentcategory data may be configured such that the first selection via theuser interface of the portable recording device corresponds to thehighest-ranking incident category of the ordered ranking of incidentcategories.

FIG. 7 illustrates a process for generating and transmitting incidentcategory data to a portable recording device based at least in part onsensor data captured from a portable recording device. Process 700 isdescribed from the perspective of the categorization controller. Inprocess 700, the categorization controller may interface with at leastthe portable recording device.

At 702, the categorization controller may monitor a portable recordingdevice associated with a user (e.g., law enforcement officer) dispatchedto a real-time incident. The monitoring activity may comprise capturingsensor data from the portable recording device at the geolocation, orproximate to the geolocation, of a real-time incident. Monitoring mayoccur continuously, per a predetermined schedule, or in response to atriggering event. Without limitation, the predetermined schedule maycorrespond to any time interval, such as one minute, five minutes, 20minutes, 30 minutes, or one hour. The triggering event may correspond toreceipt of a message from the network operations center indicating thatthe portable recording device (and law enforcement officer) has beendispatched to a real-time incident at the geolocation.

At 704, the categorization controller may interrogate sensor datacaptured from the portable recording device as part of the monitoringactivity. The sensor data may include the geolocation of the portablerecording device (which, further corresponds to a geolocation of thereal-time incident), audio data recorded via the portable recordingdevice by the law enforcement officer, and a real-time multimedia streamof the surrounding environment proximate to the portable recordingdevice (which, by extension corresponds to a real-time multimedia streamof the real-time incident).

At 706, the categorization controller may employ one or moremachine-learning algorithms to infer a listing of one or more incidentcategories that likely describe the real-time incident, based at leastin part on the sensor data. For example, if the sensor data includes amultimedia stream of individuals in “heated quarrel,” the categorizationcontroller may analyze the images and audio of the multimedia stream andthe geolocation to infer that the real-time incident likely correspondsto a “public nuisance,” “domestic disturbance,” “trespass”, or any othersuitable incident category.

In other embodiments, the categorization controller may infer thelisting of one or more incident categories, based on the sensor data andadditional data (e.g., CAD identifier, environmental data, or a suitablecombination of both).

At 708, the categorization controller may further employ one or moremachine-learning algorithms to infer an ordered ranking of the incidentcategories, based at least in part on the sensor data. The orderedranking is intended to represent the likelihood that the real-timeincident corresponds to a particular incident category. For example, ifthe geolocation corresponds to a residential area, then thecategorization controller may infer that the likely incident category ofthe “heated quarrel” is a “domestic disturbance.” In this example, thecategorization controller may assign a superior ranking to “domesticdisturbance” relative to the rankings of “public nuisance,” “trespass,”and any other suitable incident category.

In other embodiments, the categorization controller may infer theordered ranking of incident categories, based on the sensor data andadditional data (e.g., CAD identifier, environmental data, or a suitablecombination of both).

At 710, the categorization controller may generate incident categorydata that comprises the ordered ranking of the incident categories. Theincident data may include one incident category or an ordered ranking ofany number of incident categories.

At 712, the categorization controller may transmit the incident categorydata to the portable recording device that has been dispatched to thereal-time incident. The incident category data may includecomputer-executable instructions that are configured to present theordered ranking of the incident categories via a user interface (e.g.,category selector) of the portable recording device. The incidentcategory data may be configured such that the first selection via theuser interface of the portable recording device corresponds to thehighest-ranking incident category of the ordered ranking of incidentcategories.

FIG. 8 illustrates a process for generating and transmitting incidentcategory data to a portable recording device based at least in part onan audio data recording. Process 800 is described from the perspectiveof the categorization controller. In process 800, the categorizationcontroller may interface with at least the portable recording device.

At 802, the categorization controller may monitor a portable recordingdevice associated with a user (e.g., law enforcement officer) dispatchedto a real-time incident. The monitoring activity may comprise capturingsensor data from the portable recording device at the geolocation, orproximate to the geolocation, of a real-time incident. Monitoring mayoccur continuously, per a predetermined schedule, or in response to atriggering event. Without limitation, the predetermined schedule maycorrespond to any time interval, such as one minute, five minutes, 20minutes, 30 minutes, or one hour. The triggering event may correspond toreceipt of a message from the network operations center indicating thatthe portable recording device (and law enforcement officer) has beendispatched to a real-time incident at the geolocation.

At 804, the categorization controller may detect a conclusion of areal-time incident at the portable recording device. The conclusion maybe signposted by an end of a multimedia stream captured via the portablerecording device.

At 806, the categorization controller may transmit a message to theportable recording device that prompts the user (e.g., law enforcementofficer) to vocalize an incident category associated with the real-timeincident.

At 808, the categorization controller may receive, from the portablerecording device, audio data from the user (e.g., law enforcementofficer), describing the real-time incident. In one embodiment, theaudio data may comprise an annunciation of pseudonymous identifiersknown to law enforcement personnel as describing incident categories.For example, in some jurisdictions, a “10-99” code means “cardiacarrest/officer held hostage” or “10-107” means “suspicious person.” Inanother embodiment, the audio data may comprise an annunciation oflaymen terms describing the actual real-time incident. For example, thedescription may be “cardiac arrest” or “officer held hostage.”

At 810, the categorization controller may employ one or moremachine-learning algorithms to infer a listing of one or more incidentcategories that likely describe the real-time incident. In someexamples, the audio data may ambiguously describe multiple incidentcategories. For example, the pseudonymous identifier “10-99” describes“cardiac arrest” and “officer held hostage.” In these instances, thecategorization controller may infer a plurality of incident categories,rather than one incident category.

At 812, the categorization controller may further employ one or moremachine-learning algorithms to infer an ordered ranking of the incidentcategories, based at least in part on the audio data. The orderedranking is intended to represent the likelihood that the real-timeincident corresponds to a particular incident category. For example,accompanying annunciation of “he's not breathing” may imply that a“10-99” code is more likely to relate to “cardiac arrest” than to“officer held hostage.” In this example, the categorization controllermay assign a superior ranking to the “cardiac arrest” incident categoryrelative to the “officer held hostage” incident category.

In other embodiments, the categorization controller may infer theordered ranking categories based on the audio data and additional data.Additional data may comprise other sensor data captured from theportable recording device (e.g., geolocation, multimedia stream, etc.),a CAD identifier retrieved from the network operations center,environmental data (e.g., news reports, social media posts, weatherreports) retrieved from third-party servers, or any suitable combinationthereof.

At 814, the categorization controller may generate incident categorydata that comprises the ordered ranking of the incident categories. Theincident category data may include one incident category or an orderedranking of any number of incident categories.

At 816, the categorization controller may transmit the incident categorydata to the portable recording device that has been dispatched to thereal-time incident. The incident category data may includecomputer-executable instructions that are configured to present theordered ranking of the incident categories via a user interface (e.g.,category selector) of the portable recording device. The incidentcategory data may be configured such that the first selection via theuser interface of the portable recording device corresponds to thehighest-ranking incident category of the ordered ranking of incidentcategories.

CONCLUSION

Although the subject matter has been described in language specific tofeatures and methodological acts, it is to be understood that thesubject matter defined in the appended claims is not necessarily limitedto the specific features or acts described herein. Rather, the specificfeatures and acts are disclosed as exemplary forms of implementing theclaims.

What is claimed:
 1. A system, comprising: one or more processors; memorycoupled to the one or more processors, the memory including one or moremodules that are executable by the one or more processors to: detect anactivation of a portable recording device, the portable recording deviceto capture a real-time multimedia stream of a current event; identify aset of categories that is likely associated with the real-timemultimedia stream; determine an ordered ranking of individual incidentcategories of the set of categories that is likely associated with thereal-time multimedia stream; generate a ranked category dataset fordelivery to the portable recording device, the ranked category datasetto present the ordered ranking of the individual incident categories ata user interface of the portable recording device; and send the rankedcategory dataset to the portable recording device.
 2. The system ofclaim 1, wherein the one or more modules are further executable by theone or more processors to: retrieve, from a network operationscontroller, a computer-aided dispatch (CAD) identifier associated withthe current event, and wherein, to identify the set of categories isbased at least in part on the CAD identifier.
 3. The system of claim 1,wherein the one or more modules are further executable by the one ormore processors to: retrieve, from a third-party server, environmentaldata associated with the current event, and wherein, to identify the setof categories is based at least in part on the environmental data. 4.The system of claim 1, wherein the one or more modules are furtherexecutable by the one or more processors to: determine, at a point intime of the activation, a geolocation of the portable recording device;retrieve, from a third-party server, environmental data associated withthe geolocation, and wherein, to identify the set of categories is basedat least in part on the environmental data.
 5. The system of claim 1,wherein the one or more modules are further executable by the one ormore processors to: receive, from the portable recording device, sensordata associated with a surrounding environment proximate to the portablerecording device, and wherein, to identify the set of categories isbased at least in part on the sensor data.
 6. The system of claim 1,wherein the one or more modules are further executable by the one ormore processors to: receive, from the portable recording device, audiodata that describes the current event, and wherein, to identify the setof categories is based at least in part on analysis of the audio data.7. The system of claim 1, wherein the one or more modules are furtherexecutable by the one or more processors to: generate a data model toinfer the ordered ranking of the individual incident categories, basedat least in part on historical event data; and receive input data thatis associated with the current event, the input data including at leastone of a CAD identifier, environmental data, or sensor data associatedwith the portable recording device, and wherein, to determine theordered ranking of individual incident categories is based at least inpart on an analysis of the input data using the data model.
 8. Thesystem of claim 7, wherein the one or more modules are furtherexecutable by the one or more processors to: generate an accuracy scorefor individual incident categories of the set of categories, based atleast in part on the analysis of the input data, the accuracy score toindicate a likelihood that the individual incident categories areassociated with the current event, and wherein to determine the orderedranking of individual incident categories is further based at least inpart on the accuracy score of the individual incident categories.
 9. Thesystem of claim 7, wherein the ranked category data set further includescomputer-executable instructions that cause the ordered ranking ofindividual incident categories to be presented for selection via acategory selector of the portable recording device, wherein a firstselection of the category selector corresponds to a superior rankedindividual incident category of the ordered ranking of individualincident categories.
 10. One or more non-transitory computer-readablemedia collectively storing computer-executable instructions that, whenexecuted with one or more processors, collectively cause computers toperform acts comprising: detecting an activation of a portable recordingdevice that is configured to capture a real-time multimedia stream of acurrent event; retrieving, from a network operations center, acomputer-aided dispatch (CAD) identifier; identifying a set ofcategories that are likely associated with the real-time multimediastream, based at least in part on the CAD identifier; and determining anordered ranking of individual incident categories of the set ofcategories that is likely associated with the real-time multimediastream; generating a ranked category dataset for delivery to theportable recording device, the ranked category dataset to present anordered ranking of the set of categories based at least in part onsensor data; and sending the ranked category dataset to the portablerecording device.
 11. The one or more non-transitory computer-readablemedia of claim 10, wherein acts further comprise: retrieving, from theportable recording device, sensor data associated with a surroundingenvironment proximate to the portable recording device, and wherein,determining the ordered ranking of the individual incident categories isbased at least in part on the sensor data.
 12. The one or morenon-transitory computer-readable media of claim 11, wherein the sensordata comprises at least one of a geolocation or the real-time multimediastream.
 13. The one or more non-transitory computer-readable media ofclaim 10, wherein acts further comprise: retrieving from a third-partyserver, environmental data associated with a geolocation of the portablerecording device, and wherein, determining the ordered ranking of theindividual incident categories is based at least in part on theenvironmental data.
 14. The one or more non-transitory computer-readablemedia of claim 10, wherein acts further comprise: generating a datamodel to determine the ordered ranking of the individual incidentcategories, based at least in part on historical event data; andreceiving input data associated with the current event, the input datacorresponding to one of the CAD identifier, environmental data, orsensor data associated with the portable recording device, and wherein,determining the ordered ranking of individual incident categories isbased at least in part on an analysis of the input data using the datamodel.
 15. The one or more non-transitory computer-readable media ofclaim 14, wherein acts further comprising: generating an accuracy scorefor individual incident categories of the set of categories, based atleast in part on the analysis of the input data using the data model,and wherein, determining the ordered ranking of individual incidentcategories is further based at least in part on the accuracy score ofthe individual incident categories.
 16. A portable recording device,comprising: a user interface; one or more sensors; one or moreprocessors; memory coupled to the one or more processors, the memoryincluding one or more modules that are executable by the one or moreprocessors to: capture, via the one or more sensors, sensor dataassociated with a surrounding environment proximate to the portablerecording device; transmit the sensor data to a categorizationcontroller; and receive, from the categorization controller, a rankedcategory dataset, based at least in part on the sensor data, the rankedcategory dataset being configured to present, at the user interface, anordered ranking of individual incident categories of a set of categoriesthat is likely associated with a real-time multimedia stream.
 17. Theportable recording device of claim 16, wherein the one or more sensorsare further configured to capture the real-time multimedia stream of acurrent event.
 18. The portable recording device of claim 16, whereinthe user interface comprises a category selector, and wherein the rankedcategory data set includes computer-executable instructions that causean ordered ranking of individual incident categories to be presented forselection via the category selector, a first selection of the categoryselector corresponding to a superior ranked individual incident categoryof the ordered ranking of individual incident categories.
 19. Theportable recording device of claim 16, wherein the sensor data comprisesaudio data that anecdotally describes a current event at a geolocationof the portable recording device.
 20. The portable recording device ofclaim 16, wherein an individual incident category of the ordered rankingof individual incident categories, corresponds to a class of events, theclass of events including at least one of a domestic disturbance, atraffic infraction, violent dispute, property damage, trespass, protest,or public nuisance.